Flying Path Optimization of Rechargeable UAV for Data Collection in Wireless Sensor Networks

Zhu, Yuchao; Wang, Shaowei

doi:10.1109/lsens.2023.3237634

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…An effective cooperative sensor positioning and data gathering approach for WSN facilitated by numerous UAVs is presented in [19]. In particular, one UAV is designated as the primary data collection vehicle, while additional UAVs are utilized as backup UAVs for TDoA sensor positioning.…”

Section: Related Workmentioning

confidence: 99%

“…Most importantly, the estimates of AOA are typically skewed in a dense multipath environment. Therefore, more study is still required to fully understand the AOA-based localization mechanism for UAV positioning [19], [34].…”

Section: B Angle Of Arrival (Aoa)mentioning

confidence: 99%

“…When compared to the RSS and AOA, the TOA can perform with higher accuracy and requires no additional apparatus, which is important for UAV positioning. Figure 4 makes it evident, nevertheless, that all of the network's sensor nodes must synchronize their clocks [19], [36]. Many strategies have been put forth to reduce the influence of clock differences, in [37] In an effort to lower In mixed line-of-sight (LOS)/NLOS scenarios, the cooperative localization problem based on time of arrival (TOA) is addressed, along with the NLOS errors.…”

Section: Time Of Arrival (Toa)mentioning

confidence: 99%

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Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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Section: Related Workmentioning

confidence: 99%

Section: B Angle Of Arrival (Aoa)mentioning

confidence: 99%

Section: Time Of Arrival (Toa)mentioning

confidence: 99%

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Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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“…A similar scenario is studied in [19] with a different objective function, in which the optimization problem is aimed at minimizing the UAV total energy consumption coupled to minimum cost data collection from cluster head robots by visiting optimally a portion of the cluster head robots. In [20], the authors present an efficient framework to realize efficient data collection from WSNs, where a backup UAV carrying batteries travels alongside the primary UAV to make up for the UAV's lack of energy while the primary UAV is sent out to gather the aggregated data from cluster heads. The optimization problem is aimed at finding the minimum mission time for a complete round of data collection, which is formulated as a coordinated traveling salesman problem with battery constraints and is solved by utilizing a heuristic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

et al. 2023

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Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

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“…One illustration of such a model is the widely investigated Unmanned Aerial Vehicle (UAV) and/or unmanned surface vehicle (USV), or UAV and USV simultaneously [31][32][33]. We can consider (Figure 3) that there are four convex sets X, Y, W and Z with sensors, and four UAVs will collect the information.…”

Section: Introductionmentioning

confidence: 99%

Best Proximity Points for p–Cyclic Infimum Summing Contractions

Hristov

Ilchev

Kopanov

et al. 2023

Axioms

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We investigate fixed points for p cyclic maps by introducing a new notion of p–cyclic infimum summing maps and a generalized best proximity point for p–cyclic maps. The idea generalizes some results about best proximity points in order to widen the class of sets and maps for which we can ensure the existence and uniqueness of best proximity points. The replacement of the classical notions of best proximity points and distance between the consecutive set arises from the well-known group traveling salesman problem and presents a different approach to solving it. We illustrate the new result with a map that does not satisfy the known results about best proximity maps for p–cyclic maps.

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Flying Path Optimization of Rechargeable UAV for Data Collection in Wireless Sensor Networks

Cited by 15 publications

References 18 publications

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

Best Proximity Points for p–Cyclic Infimum Summing Contractions

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